Assessing model fit by cross-validation
Assessing model fit by cross-validation
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DOI：
10.1021/ci025626i
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发表时间：
2003-03-01
期刊：
影响因子：
--
通讯作者：
Mills, D
中科院分区：
文献类型：
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作者：
Hawkins, DM;Basak, SC;Mills, D
当QSAR模型拟合时，重要的是要验证任何拟合的模型，以检查其预测是否可能延续到模型拟合工作中未使用的新数据。有两种标准的方法可以做到这一点——使用一个单独的保留测试样本和计算上更繁琐的留一交叉验证，其中使用整个可用化合物池来拟合模型并评估其有效性。我们通过理论论证和对大型QSAR数据集的实证研究表明，当可用的样本量很小时——几十个或几十个而不是几百个——保留其中的一部分进行测试是浪费的，使用交叉验证要好得多，但要确保这是正确完成的。
When QSAR models are fitted, it is important to validate any fitted model-to check that it is plausible that its predictions will carry over to fresh data not used in the model fitting exercise. There are two standard ways of doing this-using a separate hold-out test sample and the computationally much more burdensome leave-one-out cross-validation in which the entire pool of available compounds is used both to fit the model and to assess its validity. We show by theoretical argument and empiric study of a large QSAR data set that when the available sample size is small-in the dozens or scores rather than the hundreds, holding a portion of it back for testing is wasteful, and that it is much better to use cross-validation, but ensure that this is done properly.